Network forensics Architecture Patterns: In Practice
RCCE students will learn network forensic capture and analysis including full packet capture, network flow analysis, protocol reconstruction, network-based artifact extraction, and network timeline construction. RCCE students will learn to deploy network capture infrastructure for forensic purposes, collect full packet captures and network flow data during incident investigations, reconstruct network sessions and extract transferred files, analyze DNS queries, HTTP transactions, and encrypted traffic metadata, detect data exfiltration patterns through network forensics, build network-based attack timelines, and produce network forensic reports that complement host-based investigation findings. This architecture course teaches secure system design using proven patterns, guardrails, and reference architectures. Building on core knowledge, RCCE students will learn to evaluate design options against security requirements, make informed trade-off decisions, and build systems that are resilient by design. Students gain the architectural thinking skills needed for security engineering and solution design roles.
- Security Engineers building defensive controls
- Security Analysts and Blue Team members
- Systems Administrators with security responsibilities
- GRC and Risk Professionals supporting controls
- Professionals implementing Network forensics Architecture Patterns: In Practice
- Execute hands-on tasks for network forensics
- Design a scalable privilege management architecture with policy and enforcement
- Execute hands-on tasks for learning objectives
- Execute hands-on tasks for capture & analysis skills — covering Deploy network capture infrastructure.
- Build detections and response workflows for privilege escalation, including data exfiltration patterns.
- Design a scalable privilege management architecture with policy and enforcement, including Evaluate design options vs. security needs.
- Explain Network Forensics Foundations fundamentals
- Execute hands-on tasks for what is network forensics?
- Execute hands-on tasks for why it matters — covering Attackers must traverse the network.
- Explain Forensic Data Types Overview fundamentals
- Execute hands-on tasks for full packet capture — covering Complete payload data, Network Flow Data, Metadata-level records.
- Execute hands-on tasks for pcap/pcapng formats — covering Network Flow Data, Metadata-level records.
| Module 01 | Network Forensics |
| Module 02 | Architecture Patterns: In Practice |
| Module 03 | Learning Objectives |
| Module 04 | Capture & Analysis Skills |
| Module 05 | Detection & Response Skills |
| Module 06 | Architecture Design Skills |
| Module 07 | Network Forensics Foundations |
| Module 08 | What Is Network Forensics? |
| Module 09 | Why It Matters |
| Module 10 | Forensic Data Types Overview |
| Module 11 | Full Packet Capture |
| Module 12 | PCAP/PcapNG formats |
| Module 13 | Network Flow Data |
| Module 14 | Log & Metadata |
All hands-on labs run on Rocheston Rose X OS. Students practice network forensics architecture patterns: in practice by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.
- Lab 1: Execute hands-on tasks for network forensics
- Lab 2: Design a scalable privilege management architecture with policy and enforcement
- Lab 3: Execute hands-on tasks for learning objectives
- Lab 4: Execute hands-on tasks for capture & analysis skills
- Lab 5: Build detections and response workflows for privilege escalation
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for Network forensics Architecture Patterns: In Practice, verifiable through the Rocheston certification portal.
- Full access to all course materials and slide decks
- Hands-on lab access on Rocheston Rose X OS environment
- Access to Rocheston CyberNotes
- Access to Rocheston Zelfire — EDR/XDR SIEM platform
- Access to Rocheston Raven — online cyber range exercise platform
- Access to Rocheston Vulnerability Vines AI